Comparative Efficacy of Monotherapies for Chronic Hand Eczema: A Network Meta‐Analysis Study
Bibliographic record
Abstract
INTRODUCTION: Chronic hand eczema (CHE) is a highly prevalent dermatology-related occupational hazard. This condition, which is a distinct diagnosis from atopic dermatitis (AD), can be quite symptomatic with implications for one's personal and professional life. Many therapeutic interventions exist for CHE, and the current study determined-through network meta-analyses (NMAs)-the relative efficacy of the newer treatments for CHE. METHODS: Data for eligible studies were identified after systematically reviewing the literature in PubMed and Scopus. Eligible studies were those that had an arm investigating the impact of monotherapy on 12- or 16-week changes in Hand Eczema Severity Index (HECSI) scores. We conducted sensitivity analyses where network meta-regressions were performed to (ecologically) adjust for variation due to age and sex. Base and sensitivity analyses were adjusted for disease severity at baseline. RESULTS: We identified 5 eligible studies-across which there were 10 active comparators. Among the active comparators were two treatments approved by the Food and Drug Administration (FDA) and European Medicines Agency (EMA) for the treatment of CHE, namely, alitretinoin 30 mg once daily (oral) and delgocitinib 20 mg/g twice daily (topical). We estimated pairwise relative effects using the mean difference (i.e., mean reduction) in the respective HECSI scores; furthermore, efficacy was ranked with Surface Under the Cumulative Ranking Curve (SUCRA) values. CONCLUSIONS: We attempted to contribute to the existing CHE literature by producing comparative information on its treatments' relative efficacy; moreover, we found no published NMA study on available treatments for CHE before the conduct of our work. Our results can guide clinical decision-making.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.025 | 0.041 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.037 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".